Conversation systems allow users to interact with computer systems. In order to assist the user with such interaction, some conversation systems employ query recommendation. Existing approaches for query recommendation in conversation systems include tutorials or context-sensitive help.
Tutorials are often too brief to cover all valid input requests. Existing approaches for context-sensitive help also have limitations.
For example, finite state machine-based approaches do not scale well. Depending on the granularity of the predicted classes, a decision tree-based help system may be too coarse to provide guidance on the exact wording for each problematic user request. In addition, depending on the coverage of the query corpus used in retrieval-based query recommendation systems, the most relevant pre-stored examples may not be close enough to the current user query to be useful.
Accordingly, it would be desirable for a query recommendation technique to be able to provide scalable, fine-grained, context-sensitive help on the exact wording of a user query.